With the trending field of Internet of Things (IoT) and smart homes (SHs), there are cybersecurity risks associated with it. Hence there is a need of effective and transparent intrusion detection system (IDS). Existing IDS models often lack explainability and robust and secure communication, thus creating a research gap. We experimented using the RT-IoT2022 dataset, which gives IoT network attack data. We apply information gain-based feature importance to filter out relevant features and apply machine learning models. Then we integrate LIME-based Explainable AI (XAI) to add an explainability. Classified data is passed through Quantum Channel for secure communication. We achieved a significant accuracy with XGBoost of 99.7%. Our XAI-based technique with the integration of quantum channels addresses security concerns. The proposed approach overcomes the research gap by combining explainability, high accuracy, and security measures, which help users to make security decisions. As a result, this study provides an explanation and protection against different types of attacks in SH.